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Conformed dimensions are a crucial component of the successful dimensional design. With the right dimension design and content, it is possible to compare facts from different fact tables, both within a subject area and across the enterprise. They can do more than enable drilling across; they serve as the focus for planning enterprise analytic capability. Dimensional design is usually implemented in parts. Regardless of the style, it is impractical to organize a single project that will encompass the entire enterprise.
A realistic project scope is achieved by subdividing the enterprise into subject areas and areas into projects.
At a logical level, when a series of stars share a set of common dimensions, the dimensions are referred to as conformed dimensions. Identical dimensions ensure conformance, but can take several other forms as well. Fact tables and conformed dimensions can be planned and documented in matrix format and serve as the blueprint for incremental implementation. Dimensions tables can conform in several ways.
Shared dimensions, degenerate dimension and conformed rollups are three ways.
A fourth style of conformance is less commonly accepted; it allows for overlapping dimensions. Tables that can conform when the dimension attributes of one are a subset of another are known up as a rollup dimension and a base dimension. They will not share a common surrogate key, but the common attributes must possess the same structure and consent. Degenerate dimensions can serve as the basis for conformance. The corresponding columns should consistent in the structure and content.
But it is not required that every fact table share the same set of instance combinations, as to do would force violation od sparsity.
Overlapping dimensions can also conform. Some designers prefer to avoid this situation, since it requires that multiple processes load equivalent dimensions columns in the same way. Conformed dimensions are the key to enterprise scope, serving as the infrastructure that integrates subject areas. This means that the dimensional design, including a conformance plan must be conducted as a strategic, upfront process.
The conforming dimensions are best illustrated through matrices since the number of criss-crossing relationships can easily clutter a table diagram. The matrices can describe conformance within a data mart or across the data marts. They are a central feature of dimensional data warehouse architecture, produced as part of strategic design effort. It allows individual implementation to proceed individually, ensuring they will fit together as each comes online. In a Corporate Information Factory, information is extracted from the enterprise data warehouse and organized for departmental use in data marts.
Because the data marts of the Corporate Information Factory draw their information from an integrated repository, the challenges of maintaining conformance are reduced, at least from the perspective of the dimensional modelers. The burden of bringing together disparate source is still present, nut it falls to the designers of the enterprise data warehouse. Designers of the dimensional data marts need only concern themselves with a single view of information: that provide by the enterprise data warehouse.
Conformance is still a necessity with the data mart and conformance across data marts can help avoid the need for additional data marts to cross subject areas. Stand-alone data mart lacks an enterprise context. They do not conform and the associated risk can partially mitigated by planning for conformance of a few key dimensions. The stand-alone data may exhibit conformance internally, it is likely to be incompatible with other data marts. Stand- alone data marts may be retrofitted to work with existing conformed dimensions, but this process is not trivial.
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